Machine learning (ML) has become big business in the last few years: companies are using it to make money, applied research has exploded in both industrial and academic settings, and curious developers everywhere are looking to level up their ML skills. But this newfound demand has largely outrun the supply of good methods for learning how these techniques are used in the wild. This book fills a pressing need.

Applied machine learning comprises equal parts mathematical principles and tricks pulled from a bag—it is, in other words, a true craft. Concentrating too much on either aspect at the expense of the other is a failure mode. Balance is essential.

For a long time, the best—and the only—way to learn machine learning was to pursue ...

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